Dynamic prediction models and optimization of polyacrylonitrile (PAN) stabilization processes for production of carbon fiber

Khayyam, H, Naebe, M, Zabihi, O, Zamani, R, Atkiss, S and Fox, B 2015, 'Dynamic prediction models and optimization of polyacrylonitrile (PAN) stabilization processes for production of carbon fiber', IEEE Transactions on Industrial Informatics, vol. 11, no. 4, pp. 887-896.


Document type: Journal Article
Collection: Journal Articles

Title Dynamic prediction models and optimization of polyacrylonitrile (PAN) stabilization processes for production of carbon fiber
Author(s) Khayyam, H
Naebe, M
Zabihi, O
Zamani, R
Atkiss, S
Fox, B
Year 2015
Journal name IEEE Transactions on Industrial Informatics
Volume number 11
Issue number 4
Start page 887
End page 896
Total pages 10
Publisher IEEE
Abstract Thermal stabilization process of polyacrylonitrile (PAN) is the slowest and the most energy-consuming step in carbon fiber production. As such, in industrial production of carbon fiber, this step is considered as a major bottleneck in the whole process. Stabilization process parameters are usually many in number and highly constrained, leading to high uncertainty. The goal of this paper is to study and analyze the carbon fiber thermal stabilization process through presenting several effective dynamic models for the prediction of the process. The key point with using dynamic models is that using an evolutionary search technique, the heat of reaction can be optimized. The employed components of the study are Levenberg-Marquardt algorithm (LMA)-neural network (LMA-NN), Gauss-Newton (GN)-curve fitting, Taylor polynomial method, and a genetic algorithm. The results show that the procedure can effectively optimize a given PAN fiber heat of reaction based on determining the proper values of heating ramp and temperature.
Subject Energy Generation, Conversion and Storage Engineering
Keyword(s) Genetic Algorithms
LMA-Neural Networks
Polyacrylinitrile
Prediction Models
Process Control
Thermal Stabilization
DOI - identifier 10.1109/TII.2015.2434329
Copyright notice © 2015 IEEE.
ISSN 1551-3203
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Citation counts: TR Web of Science Citation Count  Cited 10 times in Thomson Reuters Web of Science Article | Citations
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